DOCSLIB.ORG

Evaluation and Universal Machines the Eval/Apply Cycle Examining the Role of Eval Eval from Perspective of Language Designer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evaluation and Universal Machines the Eval/Apply Cycle Examining the Role of Eval Eval from Perspective of Language Designer Evaluation and universal machines The Eval/Apply Cycle Eval • What is the role of evaluation in defining a language? Exp & env • How can we use evaluation to design a language? Apply Proc & args • Eval and Apply execute a cycle that unwinds our abstractions • Reduces to simple applications of built in procedure to primitive data structures • Key: • Evaluator determines meaning of programs (and hence our language) • Evaluator is just another program!! 1/33 2/33 Examining the role of Eval Eval from perspective of language designer • From perspective of a language designer • Applicative order • From perspective of a theoretician • Dynamic vs. lexical scoping • Lazy evaluation • Full normal order • By specifying arguments • Just for pairs • Decoupling analysis from evaluation 3/33 4/33 static analysis: work done before execution Reasons to do static analysis • straight interpreter • Improve execution performance environment • avoid repeating work if expression contains loops • simplify execution engine expression interpreter value • Catch common mistakes early • garbled expression • operand of incorrect type • advanced interpreter environment • wrong number of operands to procedure or compiler • Prove properties of program • will be fast enough, won't run out of memory, etc. static expr execution value • significant current research topic analysis 5/33 6/33 1 Eval is expensive Summary of part 1 (eval '(define (fact n) • static analysis (if (= n 1) 1 (* n (fact (- n 1))))) GE) • work done before execution ==> undef • performance ... (eval '(fact 4) GE) ... • catch mistakes ... (eval '(= n 1) E1) ... • prove program properties which executes the case statement in eval four times • analyze evaluator ... (eval '(fact 3) E1) ... • static analysis: eliminate execution cost of eval ... (eval '(= n 1) E2) ... which executes the case statement in eval four times • The analyze evaluator avoids this cost 7/33 8/33 Strategy of the analyze evaluator Example of analyze: variable name lookup environment evaluator data struct scheme's environment environment pi 3.14 expr analyze EP execution value foo name pi foo Execution procedure pi analyze execution 3.14 a scheme procedure Env → anytype p: env analyze → → : expression (Env anytype) b: (lookup name env) (define (a-eval exp env) ((analyze exp) env)) 9/33 10/33 Implementing variable name lookup Implementing number analysis (define (analyze exp) • Implementing analyze-number is also easy (cond ((number? exp) (analyze-number exp)) (define (analyze-number exp) ((variable? exp) (analyze-variable exp)) (lambda (env) exp )) ... )) (black: analysis phase) (blue: execution phase) (define (analyze-variable exp) (lambda (env) (lookup-variable exp env))) (black: analysis phase) (blue: execution phase) 11/33 12/33 2 Summary of part 2 Subexpressions (hardest concept today) • output of analyze is an execution procedure (analyze ' (if (= n 1) 1 (* n (...))) ) • given an environment • produces value of expression • analysis phase: (analyze ' (= n 1) ) ==> pproc (analyze 1) ==> cproc • within analyze (analyze ' (* n (...)) )==> aproc • execution phase code appears inside (lambda (env) ...) • execution phase • all other code runs during analysis phase (pproc env) ==> #t or #f (depending on n) if #t, (cproc env) if #f, (aproc env) 13/33 14/33 Implementation of analyze-if Visualization of analyze-if exp 1 (define (analyze-if exp) (let ((pproc (analyze (if-predicate exp))) (if (= n 1) pproc ... (cproc (analyze (if-consequent exp))) 1 cproc (aproc (analyze (if-alternative exp)))) (* n (...))) aproc ... (lambda (env) (if (true? (pproc env)) p: env (cproc env) analyze b: exp (aproc env)) ))) p: env black: analysis phase blue: execution phase b: (if (true? (pproc env)) (cproc env) (aproc env)) 15/33 16/33 Your turn Implementation of analyze-definition • Assume the following procedures for definitions like (define x (+ y 1)) (define (analyze-definition exp) (let ((var (definition-variable exp)) (definition-variable exp) x (vproc (analyze (definition-value exp)))) (definition-value exp) (+ y 1) (lambda (env) (define-variable! name value env) add binding (define-variable! var (vproc env) env)))) to env • Implement analyze-definition black: analysis phase blue: execution phase • The only execution-phase work is define-variable! • The definition-value might be an arbitrary expression 17/33 18/33 3 Summary of part 3 Implementing lambda • Within analyze • Body stored in double bubble is an execution procedure • recursively call analyze on subexpressions • old make-procedure list<symbol>, expression, Env → Procedure • create an execution procedure which stores the EPs for subexpressions as local state • new make-procedure list<symbol>, (Env->anytype), Env → Procedure (define (analyze-lambda exp) (let ((vars (lambda-parameters exp)) (bproc (analyze (lambda-body exp)))) (lambda (env) (make-procedure vars bproc env)))) 19/33 20/33 Implementing apply: execution phase Implementing apply: analysis phase (define (execute-application proc args) (define (analyze-application exp) (cond (let ((fproc (analyze (operator exp))) ((primitive-procedure? proc) (aprocs (map analyze (operands exp)))) ...) (lambda (env) ((compound-procedure? proc) (execute-application ((procedure-body proc) (fproc env) (extend-environment (parameters proc) (map (lambda (aproc) (aproc env)) args aprocs)) ))) (environment proc)))) (else ...))) 21/33 22/33 Summary of part 4 What is Eval really? • In the analyze evaluator, • Suppose you were a circuit designer • double bubble stores execution procedure, not • Given a circuit diagram, you could transform it into an electric expression signal encoding the layout of the diagram • Now suppose you wanted to build a circuit that could take any such signal as input (any other circuit) and could then reconfigure itself to simulate that input circuit • What would this general circuit look like??? • Suppose instead you describe a circuit as a program • Can you build a program that takes any program as input and reconfigures itself to simulate that input program? • Sure – that’s just EVAL!! – it’s a UNIVERSAL MACHINE 23/33 24/33 4 It wasn’t always this obvious Why a Universal Machine? • “If it should turn out that the basic logics of a machine • If EVAL can simulate any machine, and if EVAL is itself a designed for the numerical solution of differential equations description of a machine, then EVAL can simulate itself coincide with the logics of a machine intended to make bills • This was our example of meval for a department store, I would regard this as the most • In fact, EVAL can simulate an evaluator for any other amazing coincidence that I have ever encountered” language • Just need to specify syntax, rules of evaluation Howard Aiken, writing in 1956 (designer of the Mark I • An evaluator for any language can simulate any other “Electronic Brain”, developed jointly by IBM and Harvard language starting in 1939) • Hence there is a general notion of computability – idea that a process can be computed independent of what language we are using, and that anything computable in one language is computable in any other language 25/33 26/33 Turing’s insight Turing’s insight • Alan Mathison Turing • Was fascinated by Godel’s incompleteness results in decidability (1933) • In any axiomatic mathematical system there are propositions that • 1912-1954 cannot be proved or disproved within the axioms of the system • In particular the consistency of the axioms cannot be proved. • Led Turing to investigate Hilbert’s Entscheidungsproblem • Given a mathematical proposition could one find an algorithm which would decide if the proposition was true of false? • For many propositions it was easy to find such an algorithm. • The real difficulty arose in proving that for certain propositions no such algorithm existed. • In general – Is there some fixed definite process which, in principle, can answer any mathematical question? • E.g., Suppose want to prove some theorem in geometry – Consider all proofs from axioms in 1 step – … in 2 steps …. 27/33 28/33 Turing’s insight The halting problem • Turing proposed a theoretical model of a simple kind of • If there is a problem that the universal machine can’t solve, machine (now called a Turing machine) and argued that then no machine can solve, and hence no effective process any “effective process” can be carried out by such a • Make list of all possible programs (all machines with 1 input) machine • Encode all their possible inputs as integers • Each machine can be characterized by its program • List their outputs for all possible inputs (as integer, error or • Programs can be coded and used as input to a machine loops forever) • Define f(n) = output of machine n on input n, plus 1 if output is • Showed how to code a universal machine a number • Wrote the first EVAL! • Define f(n) = 0 if machine n on input n is error or loops • But f can’t be computed by any program in the list!! • Yet we just described process for computing f?? • Bug is that can’t tell if a machine will always halt and produce an answer 29/33 30/33 5 The Halting theorem Turing’s history • Halting problem: Take as inputs the description of a • Published this work as a student machine M and a number n, and determine whether or not • Got exactly two requests for reprints M will halt and produce an answer when given n as an • One from Alonzo Church (professor of logic at input Princeton) • Halting theorem (Turing): There is no way to write a – Had his own formalism for notion of an effective program (for any computer, in any language)
Load more

Recommended publications
  • (Pdf) of from Push/Enter to Eval/Apply by Program Transformation
    From Push/Enter to Eval/Apply by Program Transformation MaciejPir´og JeremyGibbons Department of Computer Science University of Oxford [email protected] [email protected] Push/enter and eval/apply are two calling conventions used in implementations of functional lan- guages. In this paper, we explore the following observation: when considering functions with mul- tiple arguments, the stack under the push/enter and eval/apply conventions behaves similarly to two particular implementations of the list datatype: the regular cons-list and a form of lists with lazy concatenation respectively. Along the lines of Danvy et al.’s functional correspondence between def- initional interpreters and abstract machines, we use this observation to transform an abstract machine that implements push/enter into an abstract machine that implements eval/apply. We show that our method is flexible enough to transform the push/enter Spineless Tagless G-machine (which is the semantic core of the GHC Haskell compiler) into its eval/apply variant. 1 Introduction There are two standard calling conventions used to efficiently compile curried multi-argument functions in higher-order languages: push/enter (PE) and eval/apply (EA). With the PE convention, the caller pushes the arguments on the stack, and jumps to the function body. It is the responsibility of the function to find its arguments, when they are needed, on the stack. With the EA convention, the caller first evaluates the function to a normal form, from which it can read the number and kinds of arguments the function expects, and then it calls the function body with the right arguments.
    [Show full text]
  • Stepping Ocaml
    Stepping OCaml TsukinoFurukawa YouyouCong KenichiAsai Ochanomizu University Tokyo, Japan {furukawa.tsukino, so.yuyu, asai}@is.ocha.ac.jp Steppers, which display all the reduction steps of a given program, are a novice-friendly tool for un- derstanding program behavior. Unfortunately, steppers are not as popular as they ought to be; indeed, the tool is only available in the pedagogical languages of the DrRacket programming environment. We present a stepper for a practical fragment of OCaml. Similarly to the DrRacket stepper, we keep track of evaluation contexts in order to reconstruct the whole program at each reduction step. The difference is that we support effectful constructs, such as exception handling and printing primitives, allowing the stepper to assist a wider range of users. In this paper, we describe the implementationof the stepper, share the feedback from our students, and show an attempt at assessing the educational impact of our stepper. 1 Introduction Programmers spend a considerable amount of time and effort on debugging. In particular, novice pro- grammers may find this process extremely painful, since existing debuggers are usually not friendly enough to beginners. To use a debugger, we have to first learn what kinds of commands are available, and figure out which would be useful for the current purpose. It would be even harder to use the com- mand in a meaningful manner: for instance, to spot the source of an unexpected behavior, we must be able to find the right places to insert breakpoints, which requires some programming experience. Then, is there any debugging tool that is accessible to first-day programmers? In our view, the algebraic stepper [1] of DrRacket, a pedagogical programming environment for the Racket language, serves as such a tool.
    [Show full text]
  • A Practical Introduction to Python Programming
    A Practical Introduction to Python Programming Brian Heinold Department of Mathematics and Computer Science Mount St. Mary’s University ii ©2012 Brian Heinold Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported Li- cense Contents I Basics1 1 Getting Started 3 1.1 Installing Python..............................................3 1.2 IDLE......................................................3 1.3 A first program...............................................4 1.4 Typing things in...............................................5 1.5 Getting input.................................................6 1.6 Printing....................................................6 1.7 Variables...................................................7 1.8 Exercises...................................................9 2 For loops 11 2.1 Examples................................................... 11 2.2 The loop variable.............................................. 13 2.3 The range function............................................ 13 2.4 A Trickier Example............................................. 14 2.5 Exercises................................................... 15 3 Numbers 19 3.1 Integers and Decimal Numbers.................................... 19 3.2 Math Operators............................................... 19 3.3 Order of operations............................................ 21 3.4 Random numbers............................................. 21 3.5 Math functions............................................... 21 3.6 Getting
    [Show full text]
  • Making a Faster Curry with Extensional Types
    Making a Faster Curry with Extensional Types Paul Downen Simon Peyton Jones Zachary Sullivan Microsoft Research Zena M. Ariola Cambridge, UK University of Oregon [email protected] Eugene, Oregon, USA [email protected] [email protected] [email protected] Abstract 1 Introduction Curried functions apparently take one argument at a time, Consider these two function definitions: which is slow. So optimizing compilers for higher-order lan- guages invariably have some mechanism for working around f1 = λx: let z = h x x in λy:e y z currying by passing several arguments at once, as many as f = λx:λy: let z = h x x in e y z the function can handle, which is known as its arity. But 2 such mechanisms are often ad-hoc, and do not work at all in higher-order functions. We show how extensional, call- It is highly desirable for an optimizing compiler to η ex- by-name functions have the correct behavior for directly pand f1 into f2. The function f1 takes only a single argu- expressing the arity of curried functions. And these exten- ment before returning a heap-allocated function closure; sional functions can stand side-by-side with functions native then that closure must subsequently be called by passing the to practical programming languages, which do not use call- second argument. In contrast, f2 can take both arguments by-name evaluation. Integrating call-by-name with other at once, without constructing an intermediate closure, and evaluation strategies in the same intermediate language ex- this can make a huge difference to run-time performance in presses the arity of a function in its type and gives a princi- practice [Marlow and Peyton Jones 2004].
    [Show full text]
  • Advanced Tcl E D
    PART II I I . A d v a n c Advanced Tcl e d T c l Part II describes advanced programming techniques that support sophisticated applications. The Tcl interfaces remain simple, so you can quickly construct pow- erful applications. Chapter 10 describes eval, which lets you create Tcl programs on the fly. There are tricks with using eval correctly, and a few rules of thumb to make your life easier. Chapter 11 describes regular expressions. This is the most powerful string processing facility in Tcl. This chapter includes a cookbook of useful regular expressions. Chapter 12 describes the library and package facility used to organize your code into reusable modules. Chapter 13 describes introspection and debugging. Introspection provides information about the state of the Tcl interpreter. Chapter 14 describes namespaces that partition the global scope for vari- ables and procedures. Namespaces help you structure large Tcl applications. Chapter 15 describes the features that support Internationalization, includ- ing Unicode, other character set encodings, and message catalogs. Chapter 16 describes event-driven I/O programming. This lets you run pro- cess pipelines in the background. It is also very useful with network socket pro- gramming, which is the topic of Chapter 17. Chapter 18 describes TclHttpd, a Web server built entirely in Tcl. You can build applications on top of TclHttpd, or integrate the server into existing appli- cations to give them a web interface. TclHttpd also supports regular Web sites. Chapter 19 describes Safe-Tcl and using multiple Tcl interpreters. You can create multiple Tcl interpreters for your application. If an interpreter is safe, then you can grant it restricted functionality.
    [Show full text]
  • CSE341, Fall 2011, Lecture 17 Summary
    CSE341, Fall 2011, Lecture 17 Summary Standard Disclaimer: This lecture summary is not necessarily a complete substitute for attending class, reading the associated code, etc. It is designed to be a useful resource for students who attended class and are later reviewing the material. This lecture covers three topics that are all directly relevant to the upcoming homework assignment in which you will use Racket to write an interpreter for a small programming language: • Racket's structs for defining new (dynamic) types in Racket and contrasting them with manually tagged lists [This material was actually covered in the \Monday section" and only briefly reviewed in the other materials for this lecture.] • How a programming language can be implemented in another programming language, including key stages/concepts like parsing, compilation, interpretation, and variants thereof • How to implement higher-order functions and closures by explicitly managing and storing environments at run-time One-of types with lists and dynamic typing In ML, we studied one-of types (in the form of datatype bindings) almost from the beginning, since they were essential for defining many kinds of data. Racket's dynamic typing makes the issue less central since we can simply use primitives like booleans and cons cells to build anything we want. Building lists and trees out of dynamically typed pairs is particularly straightforward. However, the concept of one-of types is still prevalent. For example, a list is null or a cons cell where there cdr holds a list. Moreover, Racket extends its Scheme predecessor with something very similar to ML's constructors | a special form called struct | and it is instructive to contrast the two language's approaches.
    [Show full text]
  • Specialising Dynamic Techniques for Implementing the Ruby Programming Language
    SPECIALISING DYNAMIC TECHNIQUES FOR IMPLEMENTING THE RUBY PROGRAMMING LANGUAGE A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2015 By Chris Seaton School of Computer Science This published copy of the thesis contains a couple of minor typographical corrections from the version deposited in the University of Manchester Library. [email protected] chrisseaton.com/phd 2 Contents List of Listings7 List of Tables9 List of Figures 11 Abstract 15 Declaration 17 Copyright 19 Acknowledgements 21 1 Introduction 23 1.1 Dynamic Programming Languages.................. 23 1.2 Idiomatic Ruby............................ 25 1.3 Research Questions.......................... 27 1.4 Implementation Work......................... 27 1.5 Contributions............................. 28 1.6 Publications.............................. 29 1.7 Thesis Structure............................ 31 2 Characteristics of Dynamic Languages 35 2.1 Ruby.................................. 35 2.2 Ruby on Rails............................. 36 2.3 Case Study: Idiomatic Ruby..................... 37 2.4 Summary............................... 49 3 3 Implementation of Dynamic Languages 51 3.1 Foundational Techniques....................... 51 3.2 Applied Techniques.......................... 59 3.3 Implementations of Ruby....................... 65 3.4 Parallelism and Concurrency..................... 72 3.5 Summary............................... 73 4 Evaluation Methodology 75 4.1 Evaluation Philosophy
    [Show full text]
  • Language Design and Implementation Using Ruby and the Interpreter Pattern
    Language Design and Implementation using Ruby and the Interpreter Pattern Ariel Ortiz Information Technology Department Tecnológico de Monterrey, Campus Estado de México Atizapán de Zaragoza, Estado de México, Mexico. 52926 [email protected] ABSTRACT to explore different language concepts and programming styles. In this paper, the S-expression Interpreter Framework (SIF) is Section 4 describes briefly how the SIF has been used in class. presented as a tool for teaching language design and The conclusions are found in section 5. implementation. The SIF is based on the interpreter design pattern and is written in the Ruby programming language. Its core is quite 1.1 S-Expressions S-expressions (symbolic expressions) are a parenthesized prefix small, but it can be easily extended by adding primitive notation, mainly used in the Lisp family languages. They were procedures and special forms. The SIF can be used to demonstrate selected as the framework’s source language notation because advanced language concepts (variable scopes, continuations, etc.) they offer important advantages: as well as different programming styles (functional, imperative, and object oriented). • Both code and data can be easily represented. Adding a new language construct is pretty straightforward. • Categories and Subject Descriptors The number of program elements is minimal, thus a parser is − relatively easy to write. D.3.4 [Programming Languages]: Processors interpreters, • run-time environments. The arity of operators doesn’t need to be fixed, so, for example, the + operator can be defined to accept any number of arguments. General Terms • There are no problems regarding operator precedence or Design, Languages. associativity, because every operator has its own pair of parentheses.
    [Show full text]
  • Functional Programming Lecture 1: Introduction
    Functional Programming Lecture 4: Closures and lazy evaluation Viliam Lisý Artificial Intelligence Center Department of Computer Science FEE, Czech Technical University in Prague [email protected] Last lecture • Functions with variable number of arguments • Higher order functions – map, foldr, foldl, filter, compose Binding scopes A portion of the source code where a value is bound to a given name • Lexical scope – functions use bindings available where defined • Dynamic scope – functions use bindings available where executed Ben Wood's slides Eval Eval is a function defined in the top level context (define x 5) (define (f y) (eval '(+ x y))) Fails because of lexical scoping Otherwise the following would be problematic (define (eval x) (eval-expanded (macro-expand x))) Eval in R5RS (eval expression environment) Where the environment is one of (interaction-environment) (global defines) (scheme-report-environment x) (before defines) (null-environment x) (only special forms) None of them allows seeing local bindings Cons (define (my-cons x y) (lambda (m) (m x y))) (define (my-car p) (p (lambda (x y) x))) (define (my-cdr s) (p (lambda (x y) y))) Currying Transforms function to allow partial application not curried 푓: 퐴 × 퐵 → 퐶 curried 푓: 퐴 → (퐵 → 퐶) not curried 푓: 퐴 × 퐵 × 퐶 → 퐷 curried 푓: 퐴 → (퐵 → 퐶 → 퐷 ) Picture: Haskell Curry from Wikipedia Currying (define (my-curry1 f) (lambda args (lambda rest (apply f (append args rest))))) (define (my-curry f) (define (c-wrap f stored-args) (lambda args (let ((all (append stored-args args)))
    [Show full text]
  • Records in Haskell
    Push/enter vs eval/apply Simon Marlow Simon Peyton Jones The question Consider the call (f x y). We can either Evaluate f, and then apply it to its arguments, or Push x and y, and enter f Both admit fully-general tail calls Which is better? Push/enter for (f x y) Stack of "pending arguments" Push y, x onto stack, enter (jump to) f f knows its own arity (say 1). It checks there is at least one argument on the stack. Grabs that argument, and executes its body, then enters its result (presumably a function) which consumes y Eval/apply for (f x y) Caller evaluates f, inspects result, which must be a function. Extracts arity from the function value. (Say it is 1.) Calls fn passing one arg (exactly what it is expecting) Inspects result, which must be a function. Extracts its arity... etc Known functions Often f is a known function let f x y = ... in ...(f 3 5).... In this case, we know f's arity statically; just load the arguments into registers and call f. This "known function" optimisation applies whether we are using push/enter or eval/apply So we only consider unknown calls from now on. Uncurried functions If f is an uncurried function: f :: (Int,Int) -> Int ....f (3,4)... Then a call site must supply exactly the right number of args So matching args expected by function with args supplied by call site is easier (=1). But we want efficient curried functions too And, in a lazy setting, can't do an efficient n- arg call for an unknown function, because it might not be strict.
    [Show full text]
  • Deriving Practical Implementations of First-Class Functions
    Deriving Practical Implementations of First-Class Functions ZACHARY J. SULLIVAN This document explores the ways in which a practical implementation is derived from Church’s calculus of _-conversion, i.e. the core of functional programming languages today like Haskell, Ocaml, Racket, and even Javascript. Despite the implementations being based on the calculus, the languages and their semantics that are used in compilers today vary greatly from their original inspiration. I show how incremental changes to Church’s semantics yield the intermediate theories of explicit substitutions, operational semantics, abstract machines, and compilation through intermediate languages, e.g. CPS, on the way to what is seen in modern functional language implementations. Throughout, a particular focus is given to the effect of evaluation strategy which separates Haskell and its implementation from the other languages listed above. 1 INTRODUCTION The gap between theory and practice can be a large one, both in terms of the gap in time between the creation of a theory and its implementation and the appearance of ideas in one versus the other. The difference in time between the presentation of Church’s _-calculus—i.e. the core of functional programming languages today like Haskell, Ocaml, Racket—and its mechanization was a period of 20 years. In the following 60 years till now, there has been further improvements in both the efficiency of the implementation, but also in reasoning about how implementations are related to the original calculus. To compute in the calculus is flexible (even non-deterministic), but real machines are both more complex and require restrictions on the allowable computation to be tractable.
    [Show full text]
  • The Ruby Intermediate Language ∗
    The Ruby Intermediate Language ∗ Michael Furr Jong-hoon (David) An Jeffrey S. Foster Michael Hicks University of Maryland, College Park ffurr,davidan,jfoster,[email protected] Abstract syntax that is almost ambiguous, and a semantics that includes Ruby is a popular, dynamic scripting language that aims to “feel a significant amount of special case, implicit behavior. While the natural to programmers” and give users the “freedom to choose” resulting language is arguably easy to use, its complex syntax and among many different ways of doing the same thing. While this ar- semantics make it hard to write tools that work with Ruby source guably makes programming in Ruby easier, it makes it hard to build code. analysis and transformation tools that operate on Ruby source code. In this paper, we describe the Ruby Intermediate Language In this paper, we present the Ruby Intermediate Language (RIL), (RIL), an intermediate language designed to make it easy to ex- a Ruby front-end and intermediate representation that addresses tend, analyze, and transform Ruby source code. As far as we are these challenges. RIL includes an extensible GLR parser for Ruby, aware, RIL is the only Ruby front-end designed with these goals in and an automatic translation into an easy-to-analyze intermediate mind. RIL provides four main advantages for working with Ruby form. This translation eliminates redundant language constructs, code. First, RIL’s parser is completely separated from the Ruby in- unravels the often subtle ordering among side effecting operations, terpreter, and is defined using a Generalized LR (GLR) grammar, and makes implicit interpreter operations explicit.
    [Show full text]